Various systems acquire data from samples and process the acquired data in order to estimate properties related to the samples. The accuracy of the estimated properties depends on the signal-to-noise ratio (SNR) of the system, the data acquisition method, and a multitude of other factors.
In magnetic resonance imaging (MRI), a sample is placed in an MRI scanner. As is known, the MRI scanner “excites” spins in the sample to a higher energy level, and subsequently detects the “relaxation” of the spins to an equilibrium condition. The behavior of the spins depends on various properties related to the sample. Hence, by observing the relaxation behavior of the spins in the sample, one can estimate various properties associated with the sample.
MRI has been used to perform functional magnetic resonance imaging (fMRI), which detects brain activity in response to one or more external stimuli. Specifically, fMRI detects changes in spin behavior for various portions of the brain, as those portions are “activated” in response to one or more stimuli. These changes are often subtle and can be affected by a myriad of factors, such as magnetic field inhomogeneities, field strength, and motion artifacts. Due to the relative difficulty in detecting such subtle changes, there is an ongoing need for improved systems and methods to estimate properties of samples.